Multi-modal fusion-enhanced fuzzy adaptive variable impedance control with improved DBN for robotic constant force blade grinding

Abstract

During the grinding of aeroengine blade edges, complex time-varying nonlinear coupling and uncertain disturbances pose challenges to the adaptive regulation of constant force grinding, reducing process stability and precision. This paper proposed a multi-modal fusion-enhanced fuzzy adaptive variable impedance control with improved deep belief network (DBN) for robotic constant force blade grinding. Specifically, the three-dimensional model and point cloud model of the blade are integrated to extract accurate geometric information and generate reference grinding trajectories. Furtherly, the DBN training hyperparameters are optimized using linear success history-based adaptive differential evolution (LSHADE). This improves the DBN configuration and overcomes the limitations of conventional DBN based force compensation with fixed network structures and single modality inputs. On this basis, a fuzzy adaptive variable impedance control method based on the improved DBN is developed. Geometric, force/pose, and error modalities are fused to dynamically adjust the force compensation term. This design enables the controller to outperform conventional adaptive variable impedance methods under strongly time-varying conditions. It improves the interaction between the robot and the environment and realizes adaptive active compliant constant-force control in robotic grinding. Comparative experiments demonstrate the stability and reliability of the proposed method. Compared with mainstream methods, the proposed method reduces the grinding force error by 66.7% and 28.6%, respectively. The key error metrics MSE, RMSE, MAPE, and MAE are reduced by more than 71% and 20%, and the average surface roughness is reduced by approximately 15.6% and 5.8%, respectively